{"id":25243205,"url":"https://github.com/macabdul9/flipr-challenge","last_synced_at":"2025-04-05T20:42:28.221Z","repository":{"id":106635400,"uuid":"248821030","full_name":"macabdul9/flipr-challenge","owner":"macabdul9","description":"flipr hackathon challenge ","archived":false,"fork":false,"pushed_at":"2020-03-23T08:06:22.000Z","size":7741,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-02-12T00:45:55.138Z","etag":null,"topics":["boosting-algorithms","covid-19","deep-learning","forecasting","lstm-neural-networks","machine-learning","neural-network","regression-models","time-series-analysis"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/macabdul9.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-03-20T18:04:16.000Z","updated_at":"2020-03-23T08:06:24.000Z","dependencies_parsed_at":"2023-09-25T03:52:51.536Z","dependency_job_id":null,"html_url":"https://github.com/macabdul9/flipr-challenge","commit_stats":{"total_commits":14,"total_committers":2,"mean_commits":7.0,"dds":0.0714285714285714,"last_synced_commit":"670f0bd85e86c2b25f0fbceb9a8226f174ceeb36"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/macabdul9%2Fflipr-challenge","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/macabdul9%2Fflipr-challenge/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/macabdul9%2Fflipr-challenge/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/macabdul9%2Fflipr-challenge/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/macabdul9","download_url":"https://codeload.github.com/macabdul9/flipr-challenge/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247399898,"owners_count":20932876,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["boosting-algorithms","covid-19","deep-learning","forecasting","lstm-neural-networks","machine-learning","neural-network","regression-models","time-series-analysis"],"created_at":"2025-02-12T00:45:58.846Z","updated_at":"2025-04-05T20:42:28.190Z","avatar_url":"https://github.com/macabdul9.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# flipr-challenge\nflipr hackathon challenge \n\n# Repository Structure \n- ./data/\n  - It has the dataset files (I should have ignored it (by mentioning it in .gitignore) \n- ./src/\n  - two notebooks corresponding to each task \n- ./predictions/\n  - three .csv files containing predicted values for Infect_prob, Diuresis value on 27-03-2020 predicted by the time series model and Infect_prob on new Diuresis value(predicted by the Time series model)\n- ./assets/\n  - containing figures plotted in the task\n\n- ## Actual Infect_prob vs Predicted Infect_prob (100 samples)\n![Actual Infect_prob vs Predicted Infect_prob](assets/actualvspredicted.svg)\n\n\n- ## Diuresis Forecasting \n![Diuresis Forecasting for 27-03-2020](assets/TimeSeriesForecasting.png)\n\n\n# Flipr Hackathon Hiring Program 4.\n\n## Module 04: Machine Learning\n\nCoronavirus disease 2019 (COVID- 19 ) is an infectious disease caused by severe acute\n\nrespiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in\n\n2019 in Wuhan, China, and has since spread globally, resulting in the 2019 – 20\n\ncoronavirus pandemic. Epidemiologists are teaming up with data scientists to stem the\n\nspread of the novel coronavirus by tapping big data, machine learning and other digital\n\ntools. The goal is to get real-time forecasts and other critical information to front-line\n\nhealth-care workers and public policy makers as the outbreak unfolds. The objective of\n\nthe Hackathon is to predict the probability of person getting infected by Covid-19.\n\n\n## Background\n\nCoronaviruses are a family of hundreds of viruses that can cause fever, respiratory\nproblems, and sometimes gastrointestinal symptoms too. The 2019 novel\ncoronavirus is one of seven members of this family known to infect humans, and\nthe third in the past three decades to jump from animals to humans. Since emerging\nin China in December, this new coronavirus has caused a global health emergency,\nsickening almost 200,000 people worldwide, and so far killing more than 9,000. As\nof March 19, about 10000 cases had been reported in the US, and 155 people have\ndied.\n\nIn Wuhan, home to 11 million people, the initial number of cases was 40,\n\nestimated by a group of researchers led by Natsuko Imai of Imperial College. The\nnumber of exposed was assumed to be 20 times this number. The basic\n\nreproduction number (BRN) is the expected number of cases directly generated\nby one case. A BRN greater than one indicates that the outbreak is self-sustaining,\n\nwhile a BRN less than one indicates that the number of new cases decreases over\ntime and eventually the outbreak will stop. Ideally, the BRN should be reduced in\n\norder to slow down an epidemic. The BRN in the first three phases was estimated\nto be 3.1, 2.6, and 1.9, respectively. In the _Cell Discovery_ article, the BRN is\nassumed to have decreased to 0.9 or 0.5 in phase IV, based on previous\n\nexperience in SARS. According to an article in _Science_ in 2003, the BRN of SARS\ndecreased from 2.7 to 0.25 after the patients were isolated and the infection\n\nstarted being controlled.\n\nThe better we can track the virus, the better we can fight it. By analyzing\ndifferent parameters responsible for the outbreak of coronavirus, we can take\ncontrolling measures in an accelerated way.\n\n\n## Problem Statement\n\nIndia has 197 Total cases, out of which there are 4 deaths reported and 173 of\nthose cases are still active. With a hope of controlling the epidemic, this\nmachine learning problem is designed to cater the need of a prediction model\nthat can predict the **probability of a person getting infected by covid- 19**.\n\nThe whole world is participating in a fight against this pandemic. The\nhealthcare data science community can have a big impact on combating this\ndisease. There have been many excellent efforts to use data\nvisualization and monte carlo simulations to help combat the spread of this\npandemic. The expected prediction model would address a complimentary\nand important aspect of health policy, identifying those most at risk. By\ncombining the efforts of these and many other excellent efforts in the\nhealthcare technology space, we hope to mitigate the effects of this terrible\ndisease.\n\nPart -01 :\n\nThe objective of the first part of the problem statement is to predict the\nprobability of a person getting infected by Covid- 19 on 20th March 2020. The\noutput file 01 should contain only people_ID and the respective infect_prob\nfor the test data.\n\nPart -02 :\n\nThe Diuresis of a person is a time-dependent parameter, for which you have to\ncome up with a Time-series prediction model. Using the Diuresis predicted by\nthe model, you need to calculate the infect_prob on 27th March 2020 for every\npeople_ID in the test data.. The output file 02 should contain only people_ID\nand the respective infect_prob on 27th March.\n\n\n```\nThere are 3 files provided:\n```\n**1. Variable_Description.xlsx** :\nThis file contains description of all the variables available in the dataset\n**2. Training_data.xlsx** :\nThis is the training dataset on which model has to be trained, which contains\nparameters of a person on 20th March 2020\n**3. Test_data.xlsx** :\nThis is the test data on which accuracy of the model will be computed\n\n## Competition Rules\n\n There should only be **one submission per participant**\n\n Privately sharing of code is not permitted. In case of plagiarism, the\nparticipant shall be disqualified\n Those attempting both the parts should send 2 separate .csv/.xlsx file,\ncontaining **people_ID** and **infect_prob** on 20th March and 27th March\nrespectively\n\n The **solution_sheet** should also be attached along with the results\n Share all your files in this Google form link:\nhttps://docs.google.com/forms/d/18SkI7vbSc-\ndHdlnjLMtYsbZZ4kN_vk5XIFxGEyp2QDc/viewform?edit_requested=true\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmacabdul9%2Fflipr-challenge","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmacabdul9%2Fflipr-challenge","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmacabdul9%2Fflipr-challenge/lists"}